Device, method, and system for transforming measurement data

Abstract

The present disclosure relates to devices, methods, and systems for transforming measurement data. An example device for transforming measurement data includes a communicator configured to receive first measurement data, the first measurement data including step height values on a semiconductor chip with a chemical mechanical polishing (CMP) process performed thereon, and to receive layout data comprising a layout included in the semiconductor chip, and a processor configured to, based on the layout data, transform the first measurement data to second measurement data, the second measurement data including step height values of the semiconductor chip with a metal deposited thereon.

1 . A device for transforming measurement data, the device comprising:
 a communicator configured to
 receive first measurement data, the first measurement data including a plurality of step height values, wherein the plurality of step height values are optically measured in units of a target region on a first semiconductor chip on which a chemical mechanical polishing (CMP) process is performed, and 
 receive layout data, the layout data including a layout in the first semiconductor chip; and 
   a processor configured to
 based on the layout data, generate density data, the density data including an image to which layout density is applied, the layout density indicating a ratio of area occupied by the layout in a unit resolution region, 
 input the density data and the first measurement data to an artificial intelligence model, and 
 predict, as an output of the artificial intelligence model, second measurement data, the second measurement data including a step height value per target region of a second semiconductor chip, the second semiconductor chip comprising the first semiconductor chip and a metal deposited on the first semiconductor chip. 
   
     
     
         2 . The device of  claim 1 , wherein the processor is configured to
 receive raw data of the first measurement data via the communicator,   remove noise from the raw data based on a noise removal algorithm, and   input, to the artificial intelligence model, the first measurement data with the noise removed therefrom.   
     
     
         3 . The device of  claim 2 , wherein the noise removal algorithm comprises fast Fourier transform (FFT), and
 wherein the processor is configured to
 transform a domain of the raw data to a frequency domain of the FFT based on the FFT, 
 remove the noise from the raw data based on reflecting at least one mask in FFT spectrum with respect to the raw data, and 
 transform the frequency domain of the FFT to the domain of the raw data. 
   
     
     
         4 . The device of  claim 1 , wherein the first measurement data includes one step height value optically measured in one target region, and the density data includes a plurality of images of a plurality of different layouts in the first semiconductor chip. 
     
     
         5 . The device of  claim 1 , wherein a size of the image comprised in the density data is greater than or equal to a size of the target region. 
     
     
         6 . The device of  claim 1 , wherein the artificial intelligence model comprises a neural network, the neural network including an input layer, a hidden layer, and an output layer. 
     
     
         7 . The device of  claim 6 , wherein the hidden layer comprises a convolution layer, a pooling layer, a fully connected layer, and a regression layer. 
     
     
         8 . The device of  claim 1 , wherein the processor is configured to train the artificial intelligence model with a learning data set based on supervised learning, the learning data set comprising the first measurement data, the density data, and the second measurement data. 
     
     
         9 . The device of  claim 8 , wherein the processor is configured to
 based on the artificial intelligence model learning according to the supervised learning, remove, based on a noise removal algorithm, noise from first raw data of the first measurement data and remove, based on the noise removal algorithm, noise from second raw data of the second measurement data, and   set, as the learning data set, the first measurement data with noise removed therefrom, the density data, and the second measurement data with noise removed therefrom.   
     
     
         10 . The device of  claim 8 , wherein the processor is configured to optimize a size of a unit resolution region based on a learning result of the artificial intelligence model. 
     
     
         11 . A method of transforming measurement data, the method comprising:
 receiving first measurement data, the first measurement data including a plurality of step height values, wherein the plurality of step height values are optically measured in units of a target region on a first semiconductor chip on which a chemical mechanical polishing (CMP) process is performed;   receiving layout data, the layout data including a layout in the first semiconductor chip;   generating density data, the density data including an image to which layout density is applied, the layout density indicating a ratio of area occupied by the layout in a unit resolution region; and   predicting, based on an artificial intelligence model and from the density data and the first measurement data, second measurement data, the second measurement data including a step height value per target region of a second semiconductor chip, the second semiconductor chip comprising the first semiconductor chip and a metal deposited on the first semiconductor chip.   
     
     
         12 . The method of  claim 11 , further comprising:
 removing noise from raw data of the first measurement data based on a noise removal algorithm; and   generating the first measurement data with the noise removed therefrom.   
     
     
         13 . The method of  claim 11 , wherein the density data includes a plurality of images of a plurality of different layouts in the first semiconductor chip. 
     
     
         14 . The method of  claim 11 , wherein the artificial intelligence model is implemented as a neural network, the neural network comprising an input layer, a convolution layer, a pooling layer, a fully connected layer, a regression layer, and an output layer. 
     
     
         15 . The method of  claim 14 , further comprising training the artificial intelligence model with a learning data set based on supervised learning, the learning data set including the first measurement data, the density data, and the second measurement data. 
     
     
         16 . A system comprising:
 an optical measurement device configured to generate first measurement data, the first measurement data including a plurality of step height values in units of a target region on a first semiconductor chip by optically measuring the first semiconductor chip on which a chemical mechanical polishing (CMP) process is performed;   a database configured to store layout data, the layout data including a layout in the first semiconductor chip; and   a measurement data transformation device configured to
 generate, based on the layout data, density data, and 
 transform, based on the first measurement data, the density data, and an artificial intelligence model, the first measurement data to second measurement data, the second measurement data including a plurality of step height values of a second semiconductor chip, the second semiconductor chip comprising the first semiconductor chip and a metal deposited on the first semiconductor chip. 
   
     
     
         17 . The system of  claim 16 , wherein the measurement data transformation device is configured to
 generate layout density, the layout density indicating a ratio of an area occupied by the layout in a unit resolution region, and   predict, based on an artificial intelligence model, the second measurement data as an output of the artificial intelligence model from the density data and the first measurement data.   
     
     
         18 . The system of  claim 16 , wherein the measurement data transformation device is configured to
 remove noise from raw data of the first measurement data based on a noise removal algorithm, and   input, to the artificial intelligence model, the first measurement data with the noise removed therefrom and the density data.   
     
     
         19 . The system of  claim 16 , wherein the first measurement data comprises one step height value optically measured in one target region, and the density data comprising a plurality of images of a plurality of different layouts in the first semiconductor chip. 
     
     
         20 . The system of  claim 16 , wherein the measurement data transformation device is configured to train the artificial intelligence model with a learning data set based on supervised learning, the learning data set comprising the first measurement data, the density data, and the second measurement data. 
     
     
         21 .- 25 . (canceled)